Comparing Bayesian spatial models: Goodness-of-smoothing criteria for assessing under- and over-smoothing.

ABSTRACT: BACKGROUND:Many methods of spatial smoothing have been developed, for both point data as well as areal data. In Bayesian spatial models, this is achieved by purposefully designed prior(s) or smoothing functions which smooth estimates towards a local or global mean. Smoothing is important for several reasons, not least of all because it increases predictive robustness and reduces uncertainty of the estimates. Despite the benefits of smoothing, this attribute is all but ignored when it comes to model selection. Traditional goodness-of-fit measures focus on model fit and model parsimony, but neglect "goodness-of-smoothing", and are therefore not necessarily good indicators of model performance. Comparing spatial models while taking into account the degree of spatial smoothing is not straightf

SUBMITTER: Duncan EW 

PROVIDER: S-EPMC7239453 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature